Time Series Prediction Based on SVM and GA

Abstract

A new time series prediction method based on support vector machine (SVM) and genetic algorithm (GA) is proposed. At first, SVM is used to partition the whole input space into several disjointed regions. Secondly, GA is adopted to determine the parameter combination of the SVM corresponding to the partitioned region obtained above. At last, the different SVM in the different input-output spaces is constructed and used to predict time series. The simulation result shows that the multiple SVM achieve significant improvement in the generalization performance in comparison with the single SVM model.